This repository contains software used in reference [1]. It will be not be maintained for long. See https://github.com/mcavallaro/rancovr for the most recent implementation. Please cite [1] if you find these repositories useful.
As a demonstration, we consider the synthetic data in case.df which have been simulated from baseline baseline.matrix (see Simulation_experiment_4baseline_England.Rmd). For convenience, the data is also stored in
the matrix simulation.matrix.
> # write.table(case.df[,1:8], file='Data/Simulation_experiment_case_df.csv', quote=T, sep=',', row.names = F)
> case.df = read.table(file='Data/Simulation_experiment_case_df.csv', sep=',', header=T)
> #
> # saveRDS(b.matrix, 'Data/Simulation_experiment_baseline_matrix.rds')
> baseline.matrix = readRDS('Data/Simulation_experiment_baseline_matrix.rds')
> #
> # saveRDS(sim2, 'Data/simulation_experiment_simulation_matrix.rds')
> simulation.matrix = readRDS('Data/simulation_experiment_simulation_matrix.rds')
> #
> # write.table(df.cases2, file='Data/Simulation_experiment_coordinates_df.csv', quote=T, sep=',', row.names = F)
> coordinate.df = read.table(file='Data/Simulation_experiment_coordinates_df.csv', sep=',', header=T)
> head(case.df)
row SAMPLE_DT_numeric postcode latitude longitude population y x
1 483 0 BB120EZ 53.79724 -2.264622 157 5982.253 -67.346355
2 488 0 BB2 1HN 53.74896 -2.496710 113 5976.884 -74.560339
3 555 0 BB8 7AR 53.86487 -2.163336 109 5989.774 -63.955308
4 973 0 BN274EW 50.88265 0.263503 234 5658.151 9.803753
5 1079 0 BS106DD 51.50526 -2.603483 37 5727.385 -92.748101
6 1142 0 BS247EQ 51.34984 -2.917601 164 5710.103 -105.092027
> baseline.matrix[1:6,1:6]
0 1 2 3
AL1 1TA 0.008866186 0.009163252 0.009446268 0.009711113
AL1 1UB 0.005344003 0.005523056 0.005693641 0.005853273
AL1 2JT 0.002793456 0.002887052 0.002976221 0.003059666
> simulation.matrix[1:6,1:6]
0 1 2 3 4 5
AL1 1TA 0 0 0 0 0 0
AL1 1UB 0 0 0 0 0 0
AL1 2JT 0 0 0 0 0 0
AL1 4XG 0 0 0 0 0 0
AL1 5DF 0 0 0 0 0 0
AL1 5JQ 0 0 0 0 0 0
> head(coordinate.df[1:3,])
Postcode latitude longitude
AL1 1TA AL1 1TA 51.74220 -0.320578
AL1 1UB AL1 1UB 51.73746 -0.316577
AL1 2JT AL1 2JT 51.73965 -0.340915
AL1 4XG AL1 4XG 51.76646 -0.317093
AL1 5DF AL1 5DF 51.75159 -0.306273
AL1 5JQ AL1 5JQ 51.74941 -0.310961
Create 100,000 cyclinders to cover the detected cases:
> source('R/Init2.R')
> cylinders = CreateCylinders(observation.matrix = simulation.matrix, baseline.matrix = baseline.matrix,
emmtype = 'sim', week.range = c(0,99), n.cylinders = 100000, coord.df=coordinate.df)
Compute the warning score for each case:
> source('R/surveillance_utilis.R)
> case.df[,'warning.score'] = apply(case.df, 1, FUN=warning.score, cylinders)
> head(case.df)
row SAMPLE_DT_numeric postcode latitude longitude population y x warning.score
1 483 0 BB120EZ 53.79724 -2.264622 157 5982.253 -67.346355 0.007518797
2 488 0 BB2 1HN 53.74896 -2.496710 113 5976.884 -74.560339 0.018292683
3 555 0 BB8 7AR 53.86487 -2.163336 109 5989.774 -63.955308 0.011764706
4 973 0 BN274EW 50.88265 0.263503 234 5658.151 9.803753 0.000000000
5 1079 0 BS106DD 51.50526 -2.603483 37 5727.385 -92.748101 0.014184397
6 1142 0 BS247EQ 51.34984 -2.917601 164 5710.103 -105.092027 0.014285714
[1] Cavallaro M, Coelho J, Ready D, Decraene V, Lamagni T, McCarthy ND, Todkill D, Keeling MJ. (2022) Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease. PLoS Comput Biol 18(11): e1010726. https://doi.org/10.1371/journal.pcbi.1010726.